Cross-View Image Optimization via Multi-Channel Attention
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Solution Overview
Problem
Existing methods for generating multi-view images using generative adversarial networks (GANs) produce images of poor quality with insufficient feature details, failing to meet practical requirements in fields like image retrieval and unmanned driving.
Innovation Solution
A cross-view image optimizing method that employs a pre-trained GAN model with a multi-dimensional feature extracting module, residual normalization, and multi-channel attention mechanisms to generate high-quality multi-view images by extracting and optimizing features across multiple dimensions and channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a pre-trained GAN model is used to generate multi-view images, then the generation process can be implemented, but the quality and detail of the generated images are poor
Solution Approach 1:
The generator is divided into two distinct generators: a first generator that extracts multi-dimensional features and generates initial cross-view images, and a second generator that optimizes these images through residual processing and multi-channel attention mechanisms. This segmentation allows each generator to specialize in specific tasks, improving overall image quality while managing complexity through modular design
Solution Approach 2:
The patent introduces multi-dimensional feature extraction by processing images across multiple dimensions (spatial, channel, and attention dimensions). The multi-channel attention mechanism operates across different feature dimensions, enabling the model to capture rich contextual information and significantly improve image quality and detail representation
2Manufacturing precision
If multi-dimensional feature extraction and multi-channel attention mechanisms are applied, then image detail and quality improve, but computational complexity increases
Solution Approach 1:
The first generator performs preliminary feature extraction and generates initial cross-view images before the second generator applies optimization. This preliminary action prepares the data in advance, allowing the second generator to focus computational resources on refinement tasks rather than processing raw input from scratch, thereby improving efficiency
Solution Approach 2:
The multi-channel attention mechanism applies attention weighting selectively to different feature channels based on their importance. Rather than uniformly processing all features, the system applies partial attention to the most relevant features, reducing unnecessary computational overhead while maintaining high image quality
Data Source
AI summary
Disclosed is a cross-view image optimizing method and apparatus, and a computer equipment and a readable storage medium. The method includes: acquiring a sample image and a pre-trained cross-view image generating model; generating an multi-dimensional cross-view image of the sample image by a multi-dimensional feature extracting module of the first generator to obtain dimension features and cross-view initial images at multiple dimensions; obtaining a multi-dimensional feature map with corresponding dimension features by the second generator; inputting the multi-dimensional feature map to a multi-channel attention module of the second generator for feature extraction and calculating a feature weight of each attention channel, obtaining attention feature images, attention images and feature weights in a preset number of the attention channels; and weighting and summing the attention images and the attention feature images of all the channels according to the feature weights, and obtaining a cross-view target image.